{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demo 05: Algorithms01"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "This demo will demonstrate the options for plotting projections and images on TIGRE. The functions have been in previous demos, but in here an exaustive explanation and usage of them is given."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tigre\n",
    "geo=tigre.geometry_default(high_quality=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data and generate projections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tigre.utilities.Ax import Ax\n",
    "from tigre.demos.Test_data import data_loader\n",
    "# define angles\n",
    "angles=np.linspace(0,2*np.pi,dtype=np.float32)\n",
    "# load head phantom data\n",
    "head=data_loader.load_head_phantom(number_of_voxels=geo.nVoxel)\n",
    "# generate projections\n",
    "projections=Ax(head,geo,angles,'interpolated')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Usage of FDK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function FDK in module tigre.algorithms.single_pass_algorithms:\n",
      "\n",
      "FDK(proj, geo, angles, **kwargs)\n",
      "      solves CT image reconstruction.\n",
      "    \n",
      "      :param proj: np.array(dtype=float32),\n",
      "       Data input in the form of 3d\n",
      "    \n",
      "      :param geo: tigre.utilities.geometry.Geometry\n",
      "       Geometry of detector and image (see examples/Demo code)\n",
      "    \n",
      "      :param angles: np.array(dtype=float32)\n",
      "       Angles of projection, shape = (nangles,3) or (nangles,)\n",
      "    \n",
      "      :param filter: str\n",
      "       Type of filter used for backprojection\n",
      "       opts: \"shep_logan\"\n",
      "             \"cosine\"\n",
      "             \"hamming\"\n",
      "             \"hann\"\n",
      "    \n",
      "      :param verbose: bool\n",
      "       Feedback print statements for algorithm progress\n",
      "    \n",
      "      :param kwargs: dict\n",
      "       keyword arguments\n",
      "    \n",
      "      :return: np.array(dtype=float32)\n",
      "    \n",
      "      Usage:\n",
      "      -------\n",
      "      >>> import tigre\n",
      "      >>> import tigre.algorithms as algs\n",
      "      >>> import numpy\n",
      "      >>> from tigre.demos.Test_data import data_loader\n",
      "      >>> geo = tigre.geometry(mode='cone',default_geo=True,\n",
      "      >>>                         nVoxel=np.array([64,64,64]))\n",
      "      >>> angles = np.linspace(0,2*np.pi,100)\n",
      "      >>> src_img = data_loader.load_head_phantom(geo.nVoxel)\n",
      "      >>> proj = tigre.Ax(src_img,geo,angles)\n",
      "      >>> output = algs.FDK(proj,geo,angles)\n",
      "    \n",
      "      tigre.demos.run() to launch ipython notebook file with examples.\n",
      "    \n",
      "    \n",
      "      --------------------------------------------------------------------\n",
      "      This file is part of the TIGRE Toolbox\n",
      "    \n",
      "      Copyright (c) 2015, University of Bath and\n",
      "                          CERN-European Organization for Nuclear Research\n",
      "                          All rights reserved.\n",
      "    \n",
      "      License:            Open Source under BSD.\n",
      "                          See the full license at\n",
      "                          https://github.com/CERN/TIGRE/license.txt\n",
      "    \n",
      "      Contact:            tigre.toolbox@gmail.com\n",
      "      Codes:              https://github.com/CERN/TIGRE/\n",
      "    ----------------------------------------------------------------------\n",
      "      Coded by:          MATLAB (original code): Ander Biguri\n",
      "                         PYTHON : Reuben Lindroos\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import tigre.algorithms as algs\n",
    "print(help(algs.fdk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<tigre.utilities.plotimg.plotimg instance at 0x7f740ab0a560>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "imgfdk1=algs.FDK(projections,geo,angles,filter='ram_lak')\n",
    "imgfdk2=algs.FDK(projections,geo,angles,filter='hann')\n",
    "# The look quite similar:\n",
    "tigre.plotimg(np.hstack((imgfdk1,imgfdk2)),slice=32,dim='x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the other hand it can be seen that one has bigger errors in the whole\n",
    "image while the other just in the boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<tigre.utilities.plotimg.plotimg instance at 0x7f7401a845a8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dif1=abs(head-imgfdk1)\n",
    "dif2=abs(head-imgfdk2)\n",
    "tigre.plotimg(np.hstack((dif1,dif2)),slice=32,dim='x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15+"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
